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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in

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Speaker 1: Business with Barry Ritholt on Bloomberg Radio.

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Speaker 2: This week on the podcast, yet another extra special guest.

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Speaker 2: Brian Hurst is founder, CEO, and CIO of Clear Alpha.

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Speaker 3: They are a.

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Speaker 2: Multi manager, multi strategy hedge fund that has put up

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Speaker 2: some pretty impressive numbers. His background is really fascinating. Cliff

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Speaker 2: Astness plucked him out of the ether to be one

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Speaker 2: of his first hires at the Quantitative Research Group at

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Speaker 2: Goldman Sachs. He was the first non founding partner at AQR,

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Speaker 2: the hedge fund that Asna set up, and Brian worked

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Speaker 2: there for a couple of decades before launching Clear Alpha.

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Speaker 2: He had as a fascinating perspective on where alpha comes

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Speaker 2: from as well as the entire hedge fund industry. Few

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Speaker 2: people have seen it from the unique perspective he has,

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Speaker 2: and I think he understands the challenges of creating alpha

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Speaker 2: where it comes from and managing the risk and looking

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Speaker 2: for ways to develop non correlated alpha that is both

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Speaker 2: sustainable and manageable from a behavioral perspective. I thought this

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Speaker 2: conversation was absolutely fascinating and I think you will also

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Speaker 2: with no further ado my interview with Clear Alpha's Brian Hurst.

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Speaker 4: Thank you very appreciate it.

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Speaker 3: Good to have you back here.

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Speaker 2: Last time you were on a panel, we were talking

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Speaker 2: about the rise of some emerging managers, including yourself. But

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Speaker 2: let's go back to the beginning of your career Wharton

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Speaker 2: School at the University of Pennsylvania. You graduate with a

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Speaker 2: bachelor's in economics. Was quantitative finance always career plan?

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Speaker 4: That's a great question. I think when I went to school,

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Speaker 4: I didn't even know quantity of flants was a thing,

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Speaker 4: and frankly, at that point in time, it really wasn't

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Speaker 4: much of a thing. I was taken him by my dad.

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Speaker 4: He was an accountant and CFO of a commercial real

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Speaker 4: estate company. He would take me to the office, and

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Speaker 4: I was really fascinated by business. I really wanted to

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Speaker 4: get into that. I was into computers. I learned how

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Speaker 4: to teach myself how to program and things like that.

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Speaker 4: But I wanted to get into business, and I said, Dad,

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Speaker 4: I want to get into real estate. And my dad

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Speaker 4: gave me some really good advice. He said, Brian, if

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Speaker 4: you think about finance as an org chart, real estate

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Speaker 4: is like one of the divisions and if you start

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Speaker 4: in real estate, it's hard to move up and go

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Speaker 4: to other divisions and try other things out. You should

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Speaker 4: really learn corporate finance and you can always switch to

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Speaker 4: real estate if you wanted to. And corporate fans is

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Speaker 4: kind of the top of the umbrella or the org chart.

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Speaker 4: And I said, okay, well what's corporate finance and where

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Speaker 4: are I go to learn that? He's like, well, you

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Speaker 4: should go to Wharton And then I said, well, what's Wharton?

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Speaker 4: That's how it started.

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Speaker 3: That's hilarious.

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Speaker 2: You finish up at Pennsylvania and you begin your career

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Speaker 2: at DLJ.

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Speaker 3: What sort of work.

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Speaker 2: Were you doing and what were your classmates doing? This

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Speaker 2: is the early nineties.

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Speaker 3: You started DLJ.

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Speaker 4: Yeah, I did DLJ. It was interesting. That was my

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Speaker 4: summer year between junior and senior at Warden, and they

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Speaker 4: kept me on throughout my senior year to finish up

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Speaker 4: an interesting project, which is basically automating the job of

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Speaker 4: the investment analyst that we're doing all the company working,

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Speaker 4: all the tank k's, tank q's, all the information. At

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Speaker 4: the time, there was a new company starting up and

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Speaker 4: I know on Bloomberg, but it was called fact Set

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Speaker 4: at the time. Of course, and there was a salesperson

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Speaker 4: walking around trying to get anyone to talk to them

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Speaker 4: because this is a brand new company. And I was

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Speaker 4: a summer analyst and I was like, I've got time,

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Speaker 4: I'll talk to you. And he showed me, first of

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Speaker 4: all two things. She showed me this thing called Microsoft Excel.

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Speaker 4: At the time, everybody was using Lettus one, two three,

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Speaker 4: and he showed me basically how you can type in

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Speaker 4: a ticker and that pulls in all of the financial

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Speaker 4: information right into this cheat for you before the internet,

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Speaker 4: but you know what was kind of the internet at

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Speaker 4: the time. I was like, wow, this is amazing. I

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Speaker 4: was like, this could save me hours and hours of work.

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Speaker 4: And so I went to the MD at the time

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Speaker 4: and I said, hey, I think I can automate most

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Speaker 4: of what the analysts are doing. He said, you're a

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Speaker 4: summer intern. We're not paying you much. Go at it.

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Speaker 4: And that's what I did. So I started off in that,

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Speaker 4: but I mainly learned that I didn't want to do

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Speaker 4: investment banking because it didn't hit on my course skill set,

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Speaker 4: which was like engineering back down quantitative techniques and tools.

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Speaker 2: That sounds really interesting. It's amazing to have that sort

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Speaker 2: of experience as an intern, How did you land at

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Speaker 2: Goldman Sachs.

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Speaker 4: Like everything in life that works out well, that's you know,

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Speaker 4: a lot of hard work but mostly luck. Because of

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Speaker 4: the DLJA experience, that was a good thing to have

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Speaker 4: on my resume. Cliff Asnas founder of AQR Capital, managing

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Speaker 4: partner there at the time. I think it was late twenties.

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Speaker 4: He was finishing up his PhD at the University of

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Speaker 4: Chicago and was working for Goldman Asset Management. He got

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Speaker 4: the mand to launch a new quantitative research group, and

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Speaker 4: so he wanted to hire someone who had both the

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Speaker 4: finance background and the computer science background. I had started

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Speaker 4: with a couple of friends a software business in high

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Speaker 4: school and at Penn. One of the things I did

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Speaker 4: with my roommate was we started up a hardware business,

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Speaker 4: kind of like Michael Dell, building and selling computers to

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Speaker 4: faculty and students on campus. So I had the computer

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Speaker 4: science background. Cliff had gone undergrad at Penn at Wharton also,

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Speaker 4: so he knew that we'd taken the same kind of courses.

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Speaker 4: We spoke the same language from that perspective, and I

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Speaker 4: had that technology background. So I was his first tire

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Speaker 4: as he was building out that new team. What my

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Speaker 4: other colleagues did back then, you had basically three choices

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Speaker 4: come out of Warden. It was accounting, investment, banking, and consulting.

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Speaker 4: There was really no jobs for asset management, but those

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Speaker 4: are the courses I love the most at Penn and

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Speaker 4: really wanted to pursue that. So it was a great opportunity.

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Speaker 2: So you spend three years or so at Goldman with Cliff.

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Speaker 2: By that point he had been there for a while

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Speaker 2: and decided, hey, I think I have a little more

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Speaker 2: freedom and opportunity to find launch a fund on our own.

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Speaker 2: You were there day one, you left with him, right,

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Speaker 2: tell us a little bit about what it was like

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Speaker 2: standing up AQR with aastness.

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Speaker 4: It was great. We started off this little background there

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Speaker 4: as a research group within g SAM so think cost

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Speaker 4: center and just putting some timeframes around this. This is nineteen

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Speaker 4: ninety four, which is one of the toughest years in

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Speaker 4: Goldman's history, even going back to the Great Depression. It

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Speaker 4: was the kind of year where trem and a partner

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Speaker 4: you get to put in money. Wow, which was was

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Speaker 4: it that bad a year?

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Speaker 3: I don't remember. Ninety four is a terrible market year.

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Speaker 4: That was the year where the Fed had the surprise

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Speaker 4: significant rate hike in feb I was actually on the floor.

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Speaker 2: I think bonds took a whack, but equities also wobbled

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Speaker 2: a bit.

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Speaker 4: Is that a well a bit? But yeah, it was

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Speaker 4: really a bad year for fixed income and the firm

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Speaker 4: had a lot of risk and fixing, I presume, which

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Speaker 4: led to the tough year. Yeah. So we were a

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Speaker 4: research group, cost center, and then left in people were

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Speaker 4: disappearing week by week as they were cutting down really headcount,

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Speaker 4: and so quickly we realized we've got to start gendering

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Speaker 4: some revenue. We want to say, alive. And Cliff went

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Speaker 4: to them and said, hey, we've been we built some

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Speaker 4: interesting models. We think we're good at picking stocks and

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Speaker 4: futures and things like that. We think we can trade

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Speaker 4: on this and make some money. He convinced the partnership

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Speaker 4: to give us some money. So it's basically a prop

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Speaker 4: trading effort. For a little while, it did very well.

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Speaker 4: They kept adding money to it, and then we opened

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Speaker 4: it up and turned it into a fund. It was

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Speaker 4: really Goldman's first real hedge fund coming out of g

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Speaker 4: SAM that funded very well, which really opened the door

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Speaker 4: for us to be able to leave and start up

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Speaker 4: and raise money as an independent hedge fund.

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Speaker 2: What were the specific strategies Cliff was running at g

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Speaker 2: SAM with the partner's money.

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Speaker 4: It was a multi strategy approach, but it was all quantitative.

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Speaker 4: And when I say quantitative, that means a lot of

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Speaker 4: things to different people. I think about every good investment

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Speaker 4: process is really a process, and whether people would label

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Speaker 4: as quantitative or or not is really how automated it is.

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Speaker 4: And so by quantity of I mean like really automated

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Speaker 4: downloading public data for the most part, pumping it through

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Speaker 4: some systems, and that causes you to want to buy

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Speaker 4: and sell different instruments around the world.

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Speaker 2: But you're still creating or Cliff at the time was

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Speaker 2: creating models, and the models would give him a ranked

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Speaker 2: list of Hey, the top ten stocks on this list

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Speaker 2: of a thousand are really or whatever the number is,

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Speaker 2: are things you want to look at, either getting long

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Speaker 2: or short based on whatever that model is.

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Speaker 4: That's right. So that you'd have many different signals, and

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Speaker 4: we're treading many different asset classes, and so it's like

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Speaker 4: you're saying all those signals you would givefferent weights different signals,

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Speaker 4: and those would add up to you like these things,

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Speaker 4: you don't like these things. We would trade global equities

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Speaker 4: in a bunch of different countries, but market neutral so

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Speaker 4: long as much as you are short, so you're not

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Speaker 4: taking a bet on is the market going to go

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Speaker 4: up or down. You're really taking a bet on this

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Speaker 4: group of stocks is going to perform this other group

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Speaker 4: of stocks by looking at a bunch of different characteristics.

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Speaker 4: We did that for stocks, We did it for currencies,

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Speaker 4: for commodities, you name it was. It was tradable, and

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Speaker 4: we had data we wanted to be trading it. And

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Speaker 4: that's really what the genesis of that fund was.

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Speaker 2: How long were you guys doing that before you realized, hey,

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Speaker 2: this is really going to be a successful model, And

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Speaker 2: then how much longer was it before maybe we should

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Speaker 2: do this out from under the compliance regulations of a

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Speaker 2: broker dealer.

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Speaker 4: We started that as a fund really in nineteen ninety five.

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Speaker 4: It had been trading prop for a little time with

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Speaker 4: Goldman's money, and we made money almost every month. Basically

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Speaker 4: it traded as a fund, and you think we left

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Speaker 4: in terms of a timing perspective, you know, they started

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Speaker 4: nineteen ninety five We left early nineteen ninety eight, so

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Speaker 4: it's only a couple of years in change that we

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Speaker 4: were trading this within g SAM before leaving to start

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Speaker 4: at BAQR.

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Speaker 2: So let's talk a little bit about AQR. You there

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Speaker 2: from inception, from day one. What was that transition like

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Speaker 2: from you know, I imagine at Goldman Sachs you have access

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Speaker 2: to life, lots of support, lots of tools, lots of data,

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Speaker 2: lots of everything. What's it like starting over again from

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Speaker 2: scratch in a standalone hedge fund.

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Speaker 4: I'll tell you a funny story. So I got into

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Speaker 4: a few different battles with the administration folks at Goldman

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Speaker 4: Sachs size management. If you remember, like in college, I

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Speaker 4: had a computer business where we'd like buy parts, build

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Speaker 4: computers and sell them, and so I knew how to

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Speaker 4: build my own computers. Goldman Sachs. At the time, the

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Speaker 4: standard computer that everybody had was what was called an

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Speaker 4: eight eighty six. This is like the first real PC

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Speaker 4: that IBM had out there, and you know, they were good,

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Speaker 4: but they weren't the most advanced available machines. Basically, I

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Speaker 4: went to the administration and I said, look, we need

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Speaker 4: the most advanced machines because we're trying to run a

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Speaker 4: lot of computationally intensive models and this machine we have

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Speaker 4: now is very slow at taking very long to run

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Speaker 4: our models. You can buy the latest machine at half

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Speaker 4: the price of what Goldman was paying and get twice

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Speaker 4: the performance. What I didn't realize at the time is

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Speaker 4: that when you're trying to run an organization that large

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Speaker 4: and complex.

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Speaker 3: I want everything stand you.

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Speaker 4: Can't support it unless everything's standardized. And so there was

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Speaker 4: a reason for it which I didn't understand.

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Speaker 2: That you guys can support your own hardware.

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Speaker 3: That's not that hard.

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Speaker 4: Cliff eventually persuaded them to give let us get the

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Speaker 4: new machines. But one of the big changes as you

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Speaker 4: talk about leaving a place, you know you have lots

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Speaker 4: of resources and whatnot at large organizations, but you have

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Speaker 4: limited resources at every place, no matter how big you are.

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Speaker 4: There's always trade offs that you're making when you start

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Speaker 4: up as a new firm. One thing that was a

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Speaker 4: big change is that at Goldman we had to support

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Speaker 4: lots of other groups. We were providing research advice, investment advice,

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Speaker 4: talk to clients, help them raise money in other products.

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Speaker 4: When we launched you own hedge fund. All that matter

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Speaker 4: was making money in that hedge fund, so helping that

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Speaker 4: focus was important, and we were able to buy the

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Speaker 4: latest computers that have the cost.

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Speaker 2: I'm going to bet that you did something a little

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Speaker 2: beefier than those IBM eight to eighty six is.

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Speaker 4: Yeah, I was overclocking the machines. I was doing all

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Speaker 4: the pulling, all the ways to get things to go

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Speaker 4: as fast as possible.

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Speaker 3: Huh.

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Speaker 2: Really interesting. So at AQR you juggled a lot of responsibilities.

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Speaker 2: You were a portfolio manager, researcher, head of trading, and

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Speaker 2: apparently tech geek putting machines together. What was it like

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Speaker 2: juggling all these different responsibilities.

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Speaker 4: There's a couple things I'll say about that. So one thing,

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Speaker 4: just from a personal perspective, my wife and I we

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Speaker 4: have five children together, and that's a lot to deal with.

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Speaker 4: My wife is amazing, and there's no way I would

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Speaker 4: be able to do all the stuff I do at

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Speaker 4: work if it weren't for her being amazing and handling

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Speaker 4: everything at home. So that's the first thing, and how

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Speaker 4: I get so many things done at work. I'm also

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Speaker 4: from a personality perspective, I get bored very quickly. I

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Speaker 4: like learning and doing a lot of different things. I

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Speaker 4: like being able to jump around, So to me, that's

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Speaker 4: just fun. The consequence is sleep. I don't sleep very much.

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Speaker 2: What do you mean, not very much? And you know

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Speaker 2: that only gets worse as you get older.

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Speaker 4: We usually get to sleep around one am and you know,

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Speaker 4: six six thirty something like that.

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Speaker 2: All right, so five hours, that's not terrible, not too terrible.

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Speaker 2: I've lived on six hours most of my life and

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Speaker 2: it's and you get older that that shrinks. I thought

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Speaker 2: you were referencing the five kids, because it's like, hey,

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Speaker 2: when you have five kids, you want how to juggle

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Speaker 2: a lot of different things at once because something is

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Speaker 2: always that's fine.

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Speaker 4: There's always something going on, that's for sure.

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Speaker 3: What was it like working with Cliff back in the days.

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Speaker 4: It was fun. I think Cliff's greatd a lot of

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Speaker 4: different things, but one was he hired well. He was

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Speaker 4: able to attract really talented people and then he just

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Speaker 4: let them do what they do. So he's not a micromanager.

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Speaker 4: He just lets them run with it. And so that

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Speaker 4: was a very fortunate thing for me right place, right

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Speaker 4: time in terms of being able to get a lot

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Speaker 4: of responsibility early on, and that's how I was able

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Speaker 4: to not just be a researcher building models and creating

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Speaker 4: new strategies that I'd run by Cliff and he would say, Okay,

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Speaker 4: you're doing this dumb or doing that dumb, and you

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Speaker 4: got to improve this, but also doing all the trading

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Speaker 4: by myself for the firm for the first several years,

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Speaker 4: and then eventually saying, hey, Cliff, you know I need

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Speaker 4: some help here. We need to hire, you know, someone

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Speaker 4: to run technology other than me. We need to hire

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Speaker 4: more traders than just me so that I can actually sleep.

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Speaker 4: So that's how he ran it. And it was a

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Speaker 4: lot of fun. I mean you mentioned it earlier on.

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Speaker 4: I mean Cliff hilarious and he's.

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Speaker 2: A funny guy. And it's rare to find someone who

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Speaker 2: is a quant who can communicate as eloquently as he

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Speaker 2: can and at the same time has such a devilish

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Speaker 2: sense of humor. Like that's an unusual trifecta right there.

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Speaker 4: And it's part of what makes him fantastic as an individual,

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Speaker 4: but also fantastic to work with and work for. It

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Speaker 4: made the place fun even in the tough times. And

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Speaker 4: so that's a big reason why I think a lot

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Speaker 4: of people stuck through lots of the ups and downs

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Speaker 4: that any organization has.

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Speaker 2: Let's talk a little bit about the AQR experience. The

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Speaker 2: firm seems very I almost want to say academic. They

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Speaker 2: publish a lot of white papers, they do a lot

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Speaker 2: of research, They have very specific opinions on different topics

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Speaker 2: that seem to come up in the world of finance.

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Speaker 2: How much of this intellectual firepower is part think tank

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Speaker 2: and how much of it is just Hey, if you're

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Speaker 2: going to have an investment perspective, you need to have

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Speaker 2: the intellectual underpinnings to justify it.

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Speaker 4: So I think one thing that makes acar a very

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Speaker 4: powerful is its ability to attract top talent, specifically on

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Speaker 4: the academic side. The smart people want to hang out

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Speaker 4: with other smart people. That there is definitely a network

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Speaker 4: effect that happens there. And I would say part of

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Speaker 4: the compensation you're getting indirectly by being in an organization

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Speaker 4: like that is getting exposure to all these great minds

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Speaker 4: that you can learn from, you can bounce ideas off of.

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Speaker 4: So is it a think tank? Yeah, I think it

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Speaker 4: is a think tank from that perspective, But at the

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Speaker 4: end of the day, it's a business and they're there

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Speaker 4: to make money, make money for their investors, so I

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Speaker 4: think there is a lot of focus on that as well.

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Speaker 4: So the publications, you see a lot of white papers,

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Speaker 4: and sure, I would say it rhymes with a lot

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Speaker 4: of things they do. But they obviously keep a lot

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Speaker 4: of the special sauce unpublished and use that within their funds.

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Speaker 2: But they're still writing about broad strokes. So let's talk

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Speaker 2: about a white paper that you wrote titled the Evolution

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Speaker 2: of Alpha. Tell us how has alpha evolved over the

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Speaker 2: past few decades.

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Speaker 4: Sure, this is a white paper I wrote from my

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Speaker 4: clear Apha Cio CEO hat and it really talks about

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Speaker 4: the history of the hedge fund industry, why different models

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Speaker 4: of delivering alpha, starting with let's say single strategy hedge funds,

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Speaker 4: fund of funds, multi strategy funds, and now multi strategy

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Speaker 4: multi manager are multipm funds and that's the latest evolution.

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Speaker 4: And then we talk about what we think might be

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Speaker 4: the next step, part of which we think we will drive.

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Speaker 4: So that's the point of the paper. And there's reasons

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Speaker 4: why you went from different models from one to the next,

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Speaker 4: and it has to do with a variety of things.

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Speaker 4: I can Curg. You to read the paper, it's on

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Speaker 4: our website.

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Speaker 2: But so let's follow that up. What were the drivers

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Speaker 2: of the shift from a single manager to multiple managers

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Speaker 2: to multi strategy to multi manager multi strategy.

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Speaker 3: What was the key driver of that?

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Speaker 4: Starting back this is around two thousand, let's say. Obviously

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Speaker 4: hedge funds existed before that, but that's really the point

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Speaker 4: at which at least a meaningful amount of institution investors

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Speaker 4: actually started having investments in hedge funds as like a

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Speaker 4: normal course of business. That was the year obviously that

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Speaker 4: the market sold off a lot. That was the Enron

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Speaker 4: fiasco and whatnot. A lot of Wall Street was let go,

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Speaker 4: so a lot of talent was being let go, and

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Speaker 4: much of that talent was investment analysts, research chants. The

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Speaker 4: covered stocks new stocks, deeply knew the management of those

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Speaker 4: companies deeply. So if you're an investment analyst at a

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Speaker 4: Wall Street bank, you go off and hang up a shingle,

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Speaker 4: start a single strategy hedge fund where you're picking stocks.

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Speaker 4: You had an argument why you'd have an edge because

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Speaker 4: you knew these managers these stocks deeply. And that's really

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Speaker 4: was like a Cambrian explosion of hedge funds at that

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Speaker 4: moment in time, and even to this day, I think

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Speaker 4: in terms of like sheer number count, the vast majority

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Speaker 4: of hedge funds are really stock picking hedge funds.

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Speaker 2: Long short eleven thousand hedge funds out there too.

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Speaker 4: Yeah, yeah, long short discretionary equity stockpicking hedge funds. That

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Speaker 4: models survived for a little while, but as investors were

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Speaker 4: investing in these individual kind of single strategy, single style

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Speaker 4: hedge funds, what they realize is that anyone single approach

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Speaker 4: is not very consistent. You know, it's going to go

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Speaker 4: through it's good periods and it's bad periods, and it's

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Speaker 4: hard to hang on to what I would call be

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Speaker 4: exposed to what the line item risk is. When you

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Speaker 4: have these quarterly reviews of what's going on the portfolio,

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Speaker 4: invariably the discussion is, let's talk about the things that

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Speaker 4: are down the most, and that leads to, you know,

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Speaker 4: firing managers when they're down, usually just after a environment

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Speaker 4: that was just bad for their approach, before it rebounds

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Speaker 4: and does well, you know, in the next year. So

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Speaker 4: that model, well it still exists today, is tough from

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Speaker 4: an investment to stick with. Then you switch to fund

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Speaker 4: of funds and soucial investors. You know, one stop shop,

395
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Speaker 4: buy into a fund of funds. You can get exposure

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Speaker 4: to many different strategies and styles in one vehicle. That's

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Speaker 4: what came out of that and was to address this inconsistency.

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Speaker 4: So fund of funds were more consistent than a single

399
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Speaker 4: strategy fund. But I would say the consequence and its

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Speaker 4: or the issue really is both for fund of funds

401
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Speaker 4: and really for portfolios of hedge funds that investors have.

402
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Speaker 4: It's cash inefficient. It's capital inefficient because most hedge funds

403
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Speaker 4: have a lot of cash on their balance sheet typical

404
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Speaker 4: hedge fund. It varies, but depending on top of style

405
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Speaker 4: and strategy, will have between forty and ninety percent of

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Speaker 4: the money you give them just sitting in cash.

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Speaker 2: Really, that's a giant number. Half is a giant number.

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Speaker 2: I thought you were going to go in a different direction.

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Speaker 2: I have a friend who's an allocator at a big foundation,

410
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Speaker 2: and he calls the fund of funds fund of fees

411
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Speaker 2: because you're paying layers on top of layers of fees,

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Speaker 2: and it definitely acts as as a long term drag.

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Speaker 2: But I never would have guessed that fifty plus percent

414
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Speaker 2: of assets handed to hedge funds are in cash at

415
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Speaker 2: any one time. I always assumed it was the opposite

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Speaker 2: that all right there, you know, like the one thirty

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Speaker 2: thirty funds or whichever variation you're looking at. I always

418
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Speaker 2: assume that they're leveraged up and even if they're long short,

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Speaker 2: all that money is put to work. You're saying that's

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Speaker 2: not the case.

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Speaker 4: Well, technically all the you know, they will put the

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Speaker 4: money to work in the sense of it's not pure

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Speaker 4: cash hitting there. But really there's a lot of barring power.

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Speaker 4: You love assets that you're holding, there's a tremendous amount

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Speaker 4: of barring power you can borrow against those assets that

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Speaker 4: you hold to then create a more efficient portfolio. And

427
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Speaker 4: that's where kind of multi strategy funds evolved. So multi

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Speaker 4: strategy funds gave you the benefit of many different strategies

429
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Speaker 4: and styles, yet put into the same vehicle all these

430
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Speaker 4: positions held in the same vehicle to get much more

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Speaker 4: cash efficiency, cap efficiency, higher return on capital, plus the consistency.

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Speaker 2: So I'm assuming if you're using a multi manager, multi

433
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Speaker 2: strategy approach, anyone strategy at any given time is either

434
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Speaker 2: going to be doing well or poorly, but the overall

435
00:21:19,119 --> 00:21:23,280
Speaker 2: performance of a multi strat will offset that. So it's

436
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Speaker 2: not like, hey, this guy has a bad quarter because

437
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Speaker 2: what they do is out of favor and the clients

438
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Speaker 2: pull out their cash just before the recovery. Is there

439
00:21:32,760 --> 00:21:36,160
Speaker 2: a tendency to leave money with a multi strat multi

440
00:21:36,200 --> 00:21:39,480
Speaker 2: manager approach for longer and so you don't have those

441
00:21:39,520 --> 00:21:43,840
Speaker 2: sort of bad quarter, bad month, whatever it is, because

442
00:21:44,760 --> 00:21:47,680
Speaker 2: this just isn't working now, but it'll start working eventually.

443
00:21:48,320 --> 00:21:49,960
Speaker 2: Is that the underlying thinking.

444
00:21:50,280 --> 00:21:52,879
Speaker 4: That's really the approach? In fact, a lot of successful

445
00:21:52,920 --> 00:21:57,159
Speaker 4: single manager businesses evolve to the multi strategy approach because

446
00:21:57,440 --> 00:22:02,400
Speaker 4: they recognize that lack of consistency for a single approach,

447
00:22:02,440 --> 00:22:06,360
Speaker 4: a single investing style was a threat to their own business,

448
00:22:06,480 --> 00:22:10,480
Speaker 4: and so expanding into other strategies and styles is how

449
00:22:10,520 --> 00:22:14,520
Speaker 4: a lot of these more successful single strategy funds evolved.

450
00:22:14,680 --> 00:22:17,439
Speaker 2: So it sounds like, if you're running either a multi

451
00:22:17,480 --> 00:22:21,119
Speaker 2: manager or a multi strategy or both, everything needs to

452
00:22:21,119 --> 00:22:25,160
Speaker 2: be very non correlated. You don't want everything down at

453
00:22:25,160 --> 00:22:29,320
Speaker 2: the same time. How do you approach picking various strategies

454
00:22:29,840 --> 00:22:31,480
Speaker 2: that are not correlated.

455
00:22:31,840 --> 00:22:35,400
Speaker 4: That's a great question. I think it's helpful. I don't

456
00:22:35,400 --> 00:22:37,200
Speaker 4: like the gambling angle, but I think it's a helpful

457
00:22:37,200 --> 00:22:41,399
Speaker 4: analogy because most people are used to the analogy. If

458
00:22:41,440 --> 00:22:44,680
Speaker 4: you think about the casino, people go to the casino

459
00:22:44,920 --> 00:22:47,520
Speaker 4: knowing that if they play the games long enough, they're

460
00:22:47,520 --> 00:22:49,440
Speaker 4: going to lose their money. I think most people think

461
00:22:49,480 --> 00:22:53,919
Speaker 4: that the multi strategy hedge fund is really like the

462
00:22:54,000 --> 00:22:58,400
Speaker 4: house where each table or each game in the casino

463
00:22:58,520 --> 00:23:03,200
Speaker 4: in their house has slight edge, and if they make

464
00:23:03,240 --> 00:23:06,640
Speaker 4: sure that there's not going to be massive losses at

465
00:23:06,720 --> 00:23:09,720
Speaker 4: different tables on the same night, same weekend, same month,

466
00:23:10,560 --> 00:23:15,040
Speaker 4: over time, they will just just stashysically accrue profits in

467
00:23:15,280 --> 00:23:17,600
Speaker 4: a more consistent manner. So that is a big focus.

468
00:23:17,960 --> 00:23:19,879
Speaker 4: And if you think about what risk manners would do

469
00:23:19,920 --> 00:23:21,200
Speaker 4: at a casino, it's the same thing. They're going to

470
00:23:21,280 --> 00:23:24,719
Speaker 4: make sure that these tables, these games are not going

471
00:23:24,760 --> 00:23:27,280
Speaker 4: to be making or losing money at the same time.

472
00:23:27,720 --> 00:23:32,080
Speaker 2: So let's talk about some of these diversified, non correlated strategies.

473
00:23:32,720 --> 00:23:38,440
Speaker 2: I'm assuming some include momentum, long, short, any other sort

474
00:23:38,480 --> 00:23:41,880
Speaker 2: of approaches that people would really readily understand.

475
00:23:42,240 --> 00:23:46,879
Speaker 4: Sure, when I think about most hedge fund strategies, the

476
00:23:46,880 --> 00:23:48,840
Speaker 4: ones that people know about, the ones that there are.

477
00:23:49,640 --> 00:23:52,080
Speaker 4: If you look at hedge fund indicies, there's a category

478
00:23:52,119 --> 00:23:55,040
Speaker 4: for it, you know. So it could be long short

479
00:23:55,040 --> 00:23:58,520
Speaker 4: stock picking, it could be merger arbitrage, it could be

480
00:23:59,000 --> 00:24:02,680
Speaker 4: index rebound arbitra, or basis trading. There's a variety, and

481
00:24:02,720 --> 00:24:05,240
Speaker 4: there's like dozens of these kind of well known, well

482
00:24:05,320 --> 00:24:09,840
Speaker 4: understand activists exactly. These are all out there, they're they're

483
00:24:09,840 --> 00:24:11,840
Speaker 4: well known. When you look at each one of those,

484
00:24:12,040 --> 00:24:15,920
Speaker 4: you can break it down between kind of cheap passive beta.

485
00:24:15,960 --> 00:24:19,120
Speaker 4: So let's take an example long short, discretionary stockpicking. Most

486
00:24:19,160 --> 00:24:22,679
Speaker 4: of these hedge funds, the way they're implemented is the

487
00:24:22,720 --> 00:24:26,480
Speaker 4: manager's net long the stock market, and so some portion

488
00:24:26,560 --> 00:24:28,879
Speaker 4: of their returns, it's actually a pretty sniffing portion is

489
00:24:28,920 --> 00:24:30,359
Speaker 4: just being going to be driven by whether the stock

490
00:24:30,359 --> 00:24:33,119
Speaker 4: markets separate down there just pure data, pure beta, and

491
00:24:33,960 --> 00:24:37,479
Speaker 4: that's that's a I think about the scarce resources your

492
00:24:37,520 --> 00:24:39,560
Speaker 4: risk budget, and how do you want to allocate that

493
00:24:39,640 --> 00:24:41,400
Speaker 4: risk budget. If you're allocking a lot of your risk

494
00:24:41,400 --> 00:24:44,879
Speaker 4: budget to just pure beta, that might work for the manager,

495
00:24:44,920 --> 00:24:46,560
Speaker 4: but for an investor that doesn't make a lot of

496
00:24:46,600 --> 00:24:48,600
Speaker 4: sense because I can go and get pure beta. I

497
00:24:48,640 --> 00:24:51,400
Speaker 4: can buy an index fund for you know, single digit

498
00:24:51,440 --> 00:24:56,320
Speaker 4: basis points. At this point, it's effectively free these multi

499
00:24:56,320 --> 00:24:59,280
Speaker 4: strategy funds in order to reduce the correlation across their managers.

500
00:24:59,280 --> 00:25:01,639
Speaker 4: They don't want to have all these manager's long pure beta.

501
00:25:01,880 --> 00:25:03,880
Speaker 4: That's a common risk that will cause them to make

502
00:25:03,920 --> 00:25:06,040
Speaker 4: and lose money at the same time. And so when

503
00:25:06,040 --> 00:25:08,160
Speaker 4: you're running a multi strategy fund, it's really about looking

504
00:25:08,200 --> 00:25:11,040
Speaker 4: at these common risks. BITA is the simplest example. It

505
00:25:11,080 --> 00:25:13,439
Speaker 4: could be sector exposure, it could be factor exposure like

506
00:25:13,480 --> 00:25:15,760
Speaker 4: momentum you mentioned earlier, and there's a lot of other

507
00:25:16,720 --> 00:25:20,000
Speaker 4: less well known but known in the industry risks that

508
00:25:20,080 --> 00:25:23,200
Speaker 4: take place. You know, people talk about crowding. There's reasons

509
00:25:23,200 --> 00:25:25,840
Speaker 4: why crowding happens. So being able to be aware of

510
00:25:25,880 --> 00:25:29,439
Speaker 4: those and look for signs of that and trying to

511
00:25:29,560 --> 00:25:32,800
Speaker 4: mitigate those common allies across your different strategies is a

512
00:25:32,840 --> 00:25:36,320
Speaker 4: really key component to managing risk for these multi strategy funds.

513
00:25:36,800 --> 00:25:38,760
Speaker 2: Huh, there's so many different ways to go with this.

514
00:25:39,359 --> 00:25:43,800
Speaker 2: So you're implying with these crowded funds that there's a

515
00:25:43,840 --> 00:25:46,600
Speaker 2: way to identify when when you're in a crowded fund,

516
00:25:47,320 --> 00:25:50,320
Speaker 2: I recall the quant quake a couple of years back,

517
00:25:51,119 --> 00:25:55,720
Speaker 2: where all these big quant shops post GFC really seem

518
00:25:55,880 --> 00:25:59,200
Speaker 2: like they were having the same sort of exposure in

519
00:25:59,240 --> 00:26:02,240
Speaker 2: the same sort of problem problems. How can you identify

520
00:26:02,520 --> 00:26:05,920
Speaker 2: an event like that before it takes your fun down

521
00:26:06,000 --> 00:26:06,919
Speaker 2: ten twenty percent.

522
00:26:07,400 --> 00:26:09,120
Speaker 4: That's a great question, And I would say a more

523
00:26:09,160 --> 00:26:14,200
Speaker 4: recent example might be COVID March of twenty twenty when

524
00:26:14,200 --> 00:26:17,000
Speaker 4: they're so I talked about a couple of different common risks.

525
00:26:17,040 --> 00:26:20,919
Speaker 4: One is beta one. Another one might be factors, a simple.

526
00:26:21,359 --> 00:26:24,720
Speaker 4: Other one is just there's a well known strategy. Let's

527
00:26:24,720 --> 00:26:27,600
Speaker 4: say merge arbatrage. You know there are plenty of funds

528
00:26:27,600 --> 00:26:29,680
Speaker 4: that are running merge arbatrage is one of their strategies

529
00:26:29,680 --> 00:26:32,320
Speaker 4: within the fund. Okay, simply because a lot of people

530
00:26:32,359 --> 00:26:36,240
Speaker 4: are doing something that in a sense, when there is

531
00:26:36,280 --> 00:26:38,800
Speaker 4: some other exogenous event that causes people to de risk,

532
00:26:39,600 --> 00:26:42,680
Speaker 4: it actually makes it bad to be in well known,

533
00:26:42,720 --> 00:26:47,919
Speaker 4: well understood trading strategies, so that you know ahead of

534
00:26:47,920 --> 00:26:50,399
Speaker 4: time that this is something that is crowded. You know

535
00:26:50,440 --> 00:26:52,080
Speaker 4: that there are other players that are doing the same

536
00:26:52,160 --> 00:26:54,040
Speaker 4: kind of trades as you going in.

537
00:26:54,800 --> 00:26:58,240
Speaker 2: Huh, that's really interesting, And just to put some meat

538
00:26:58,280 --> 00:27:03,560
Speaker 2: on the bones. Multi strategy, multi manager, multi model funds

539
00:27:03,720 --> 00:27:10,040
Speaker 2: have really gained prominence lately, names like Citadel, Point seventy two, Millennium,

540
00:27:10,359 --> 00:27:14,960
Speaker 2: lots of other larger funds have very much adopted this approach.

541
00:27:15,359 --> 00:27:17,000
Speaker 3: Fair statement, that's very fair.

542
00:27:17,480 --> 00:27:19,439
Speaker 4: I do think it's the best way to deliver alpha.

543
00:27:20,240 --> 00:27:24,560
Speaker 2: So you're reducing correlation, you're reducing risk, you're increasing the

544
00:27:24,560 --> 00:27:29,560
Speaker 2: odds of about performance. At how broad are firms like

545
00:27:29,640 --> 00:27:32,840
Speaker 2: I don't know, Citadel or Millennium that they don't run

546
00:27:32,880 --> 00:27:36,320
Speaker 2: into that crowded trade risk? You would think given their

547
00:27:36,359 --> 00:27:39,679
Speaker 2: size and they're tens of billions of dollars, a crowded

548
00:27:39,720 --> 00:27:42,679
Speaker 2: trade becomes increasingly more likely, right.

549
00:27:42,760 --> 00:27:44,800
Speaker 4: Right, And there's a reason for why that's the case.

550
00:27:46,320 --> 00:27:48,919
Speaker 4: There are literally thousands of different types of ways to

551
00:27:48,920 --> 00:27:51,960
Speaker 4: make money in the markets, thousands, but there's only dozens

552
00:27:52,119 --> 00:27:54,720
Speaker 4: of ways of making money in the markets that have

553
00:27:54,840 --> 00:27:56,520
Speaker 4: lots of capacity. And you can put a lot of

554
00:27:56,520 --> 00:27:58,400
Speaker 4: dollars in general, a lot of dollars to scale up,

555
00:27:58,600 --> 00:28:01,399
Speaker 4: to scale up, and if you're going to be a

556
00:28:01,520 --> 00:28:04,159
Speaker 4: very large fund, you by definition have to put more

557
00:28:04,160 --> 00:28:06,840
Speaker 4: and more of your money into the well known large

558
00:28:07,040 --> 00:28:10,080
Speaker 4: trading strategies and so they have to be particularly attuned

559
00:28:10,119 --> 00:28:12,760
Speaker 4: to the fact that they are large and their competitors

560
00:28:12,760 --> 00:28:14,520
Speaker 4: are also large, and then the same kind of trades.

561
00:28:14,680 --> 00:28:16,760
Speaker 4: So it is at risk. And when these things, you know,

562
00:28:16,800 --> 00:28:20,439
Speaker 4: when one of these shops sells out or reduces risks

563
00:28:20,520 --> 00:28:22,879
Speaker 4: and one of these common strategies, it's going to affect

564
00:28:22,880 --> 00:28:25,719
Speaker 4: the other ones. It's hard to avoid that. But they

565
00:28:25,720 --> 00:28:28,280
Speaker 4: are fairly well diversified across many different types of strategies,

566
00:28:28,320 --> 00:28:31,560
Speaker 4: so that's why you see still very consistent returns. But

567
00:28:31,680 --> 00:28:34,640
Speaker 4: there is this exogenous risk element of having being big

568
00:28:34,640 --> 00:28:37,320
Speaker 4: in the credit. The way you avoid that is by

569
00:28:37,320 --> 00:28:40,240
Speaker 4: being smaller, focusing on smaller strategies. They're a little bit different.

570
00:28:40,400 --> 00:28:40,640
Speaker 3: Huh.

571
00:28:40,720 --> 00:28:45,240
Speaker 2: Really interesting. So you mentioned earlier early days of hedge funds,

572
00:28:45,320 --> 00:28:49,160
Speaker 2: the fund of funds were popular. It feels like they're

573
00:28:49,240 --> 00:28:52,360
Speaker 2: kind of going away. You certainly hear much less about

574
00:28:52,440 --> 00:28:56,240
Speaker 2: them these days. Is that a fair assessment. Just because

575
00:28:56,280 --> 00:28:59,160
Speaker 2: you don't hear about stuff doesn't mean it's disappeared. But

576
00:28:59,520 --> 00:29:02,600
Speaker 2: I certainly only read much less about fund of funds

577
00:29:02,640 --> 00:29:06,080
Speaker 2: that they are in the news much less. Have multi manager,

578
00:29:06,160 --> 00:29:10,880
Speaker 2: multi strat multi model broad funds replaced the concept of

579
00:29:10,920 --> 00:29:11,640
Speaker 2: fund of funds.

580
00:29:12,280 --> 00:29:14,200
Speaker 4: I think as an evolution, it doesn't mean that the

581
00:29:14,200 --> 00:29:17,600
Speaker 4: fund of funds model is going away entirely. There's certain

582
00:29:17,680 --> 00:29:21,360
Speaker 4: managers out there who have commingled vehicles that only you know.

583
00:29:21,560 --> 00:29:24,000
Speaker 4: They won't run an SMA for you, they won't trade

584
00:29:24,000 --> 00:29:26,440
Speaker 4: their strategy into your account. Fund of funds can access that,

585
00:29:26,480 --> 00:29:29,360
Speaker 4: So there's a reason for that. And you know they're

586
00:29:29,440 --> 00:29:31,400
Speaker 4: nice one stop shops and they can maybe a little

587
00:29:31,440 --> 00:29:34,080
Speaker 4: more transparent. But there are You talked about this earlier,

588
00:29:34,280 --> 00:29:37,520
Speaker 4: the fees being an issue, and it's really about the

589
00:29:37,560 --> 00:29:40,600
Speaker 4: fee is a percentage of the dollars of P and

590
00:29:40,760 --> 00:29:44,760
Speaker 4: L being earned. There's an academic paper recently published that

591
00:29:44,840 --> 00:29:47,400
Speaker 4: did a really interesting study over ten years of looking

592
00:29:47,440 --> 00:29:51,000
Speaker 4: at institutional hedge fund portfolios. What it showed is that

593
00:29:51,160 --> 00:29:53,720
Speaker 4: for every dollar of P and L being generated by

594
00:29:54,000 --> 00:29:56,320
Speaker 4: these hedge fund strategies, at the end of the day,

595
00:29:56,760 --> 00:30:00,560
Speaker 4: the institutional investor took home about thirty seven cents, really,

596
00:30:00,800 --> 00:30:02,960
Speaker 4: which is I think a shocking number from right right.

597
00:30:03,040 --> 00:30:06,200
Speaker 2: So you're saying almost two thirds of the money never

598
00:30:06,760 --> 00:30:10,400
Speaker 2: either it's fees or costs or some other factor, but

599
00:30:10,680 --> 00:30:14,320
Speaker 2: only let a little more than a third ends up

600
00:30:14,680 --> 00:30:15,960
Speaker 2: with the actual investor.

601
00:30:16,280 --> 00:30:19,480
Speaker 4: That's right, and it's actually it's really interesting breaks down

602
00:30:20,560 --> 00:30:22,400
Speaker 4: the sources of all these things. Part of it is

603
00:30:22,440 --> 00:30:25,400
Speaker 4: fees and double layers of fees and things like that.

604
00:30:25,720 --> 00:30:28,280
Speaker 4: A big part of it is the behavioral nature, which

605
00:30:28,320 --> 00:30:32,320
Speaker 4: I think is driven by governance of investing organizations.

606
00:30:31,680 --> 00:30:33,160
Speaker 3: Where filled with humans.

607
00:30:33,600 --> 00:30:36,400
Speaker 4: Yes, strategy is down. What's been down, Let's get out

608
00:30:36,400 --> 00:30:37,920
Speaker 4: of that. Let's get into the thing that's been up

609
00:30:37,920 --> 00:30:41,040
Speaker 4: recently that costs about a third of your offha.

610
00:30:41,200 --> 00:30:43,880
Speaker 2: That doesn't surprise me at all, even though you expect

611
00:30:43,920 --> 00:30:47,400
Speaker 2: big endowments and foundations and hedge funds to be smarter

612
00:30:47,480 --> 00:30:50,440
Speaker 2: than that. Fillm with people and you're going to get

613
00:30:50,480 --> 00:30:52,040
Speaker 2: those behavioral problems, aren't you.

614
00:30:52,640 --> 00:30:54,840
Speaker 4: Yeah, Well, there's agency issues in between, and I think

615
00:30:54,840 --> 00:30:57,800
Speaker 4: investors are well aware of these, so that causes part

616
00:30:57,800 --> 00:31:00,280
Speaker 4: of it too. But a big thing and the thing

617
00:31:00,360 --> 00:31:05,000
Speaker 4: that kind of the multi manager, multi strategy approach tackles

618
00:31:05,160 --> 00:31:07,120
Speaker 4: that a fund of funds can't is you get a

619
00:31:07,160 --> 00:31:10,560
Speaker 4: lot of netting benefits both from you know, one manager's

620
00:31:10,560 --> 00:31:13,520
Speaker 4: long apple another manager's short apple. Right in a fund

621
00:31:13,520 --> 00:31:16,280
Speaker 4: to fund approach where you're investing in two different funds, well,

622
00:31:16,320 --> 00:31:19,960
Speaker 4: a they don't know that. And b the managers who

623
00:31:20,000 --> 00:31:22,600
Speaker 4: long Apple, they're paying a financing spread to go leverage

624
00:31:22,680 --> 00:31:25,240
Speaker 4: long Apple, and the managers's shortest paying financing spread to

625
00:31:25,240 --> 00:31:28,040
Speaker 4: go short Apples. You're paying a lot of extra cost

626
00:31:28,080 --> 00:31:30,520
Speaker 4: there just to be net flat, just to be net flat.

627
00:31:30,680 --> 00:31:33,320
Speaker 4: So if those two managers instead traded those positions into

628
00:31:33,360 --> 00:31:37,080
Speaker 4: the same vehicle, you're getting that efficiency and that's worth

629
00:31:37,160 --> 00:31:38,360
Speaker 4: you know, in the order of like two to three

630
00:31:38,360 --> 00:31:43,160
Speaker 4: percent per year just that alone. The enhanced risk management

631
00:31:43,200 --> 00:31:46,479
Speaker 4: you can get by having daily position transparency and all

632
00:31:46,480 --> 00:31:48,600
Speaker 4: the trades, if all the different pans are doing, being

633
00:31:48,640 --> 00:31:51,800
Speaker 4: able to hedge out all these beta risk factor, risk

634
00:31:51,840 --> 00:31:53,960
Speaker 4: sector risks, things like that allows you to be much

635
00:31:53,960 --> 00:31:57,320
Speaker 4: more efficient with how you deploy that capital. And so

636
00:31:57,360 --> 00:31:59,320
Speaker 4: you see that these multi manager funds tend to be

637
00:31:59,320 --> 00:32:02,560
Speaker 4: a little more invested than a hedge fund portfolio typically

638
00:32:02,640 --> 00:32:05,600
Speaker 4: could be, and that creates a lot of efficiencies. And

639
00:32:05,640 --> 00:32:07,680
Speaker 4: so when you look at the returns that they're generating,

640
00:32:07,960 --> 00:32:09,920
Speaker 4: you know, it's closer to like fifty to fifty, where

641
00:32:09,960 --> 00:32:12,560
Speaker 4: like for every dollar that's generative P and L, fifty

642
00:32:12,560 --> 00:32:14,320
Speaker 4: cents is going to the investor. So it's a much

643
00:32:14,360 --> 00:32:17,640
Speaker 4: more efficient delivery mechanism of alpha.

644
00:32:18,280 --> 00:32:21,880
Speaker 2: So we were talking earlier and I mentioned off air

645
00:32:22,000 --> 00:32:29,160
Speaker 2: that the funny element of individual investors tending to underperform

646
00:32:29,240 --> 00:32:32,680
Speaker 2: their own investments. I know you've done some research on that.

647
00:32:32,800 --> 00:32:34,280
Speaker 2: Tell us a little bit about what you say.

648
00:32:34,760 --> 00:32:37,120
Speaker 4: Yeah, this is really something that's very important to me

649
00:32:38,080 --> 00:32:41,400
Speaker 4: when I think about the industry and like, what are

650
00:32:41,440 --> 00:32:44,960
Speaker 4: the big problems that are facing the industry. What's really

651
00:32:45,720 --> 00:32:48,040
Speaker 4: causing investors not to get as much money in their

652
00:32:48,080 --> 00:32:50,960
Speaker 4: retirem accounts as we possibly could get there. One of

653
00:32:51,000 --> 00:32:54,440
Speaker 4: them is this behavioral issue, which I think also ties

654
00:32:54,480 --> 00:32:58,200
Speaker 4: to like incentives and governance and agency issues within investing organizations.

655
00:32:58,800 --> 00:33:00,840
Speaker 4: Morning Star does a study that they call Mine the Gap,

656
00:33:00,880 --> 00:33:03,880
Speaker 4: and they do it on a regular basis. Some of

657
00:33:03,880 --> 00:33:06,360
Speaker 4: your relitiers might have heard this, and it's definitely worth reading.

658
00:33:06,920 --> 00:33:09,360
Speaker 4: I'll quote some numbers off the top of my head.

659
00:33:09,560 --> 00:33:12,720
Speaker 4: I might be remembering incorrectly, but what it does is

660
00:33:12,760 --> 00:33:17,360
Speaker 4: it's measuring the time weighted returns of funds, which is

661
00:33:17,400 --> 00:33:19,400
Speaker 4: the returns that funds report. These are the returns that

662
00:33:19,440 --> 00:33:21,360
Speaker 4: if you invested a dollar at the beginning and you

663
00:33:21,360 --> 00:33:23,240
Speaker 4: held it all the way through, the returns you would

664
00:33:23,280 --> 00:33:25,480
Speaker 4: have gotten if you never went to or went out

665
00:33:25,480 --> 00:33:27,959
Speaker 4: of that fund. Then they compare that to the asset

666
00:33:27,960 --> 00:33:30,480
Speaker 4: weighted returns, right, and that is going to be the

667
00:33:30,480 --> 00:33:34,120
Speaker 4: asset weight returns are counting for the fact that you know,

668
00:33:34,280 --> 00:33:37,200
Speaker 4: the fund does well, everybody gets excited, money comes in

669
00:33:38,520 --> 00:33:41,040
Speaker 4: larger assets, and then it maybe does not as well

670
00:33:41,600 --> 00:33:45,040
Speaker 4: after that, and so the larger assets earn less return.

671
00:33:45,160 --> 00:33:46,800
Speaker 4: And so the asset way to return minus the time

672
00:33:46,840 --> 00:33:48,480
Speaker 4: way to return is a really good way to measuring

673
00:33:48,760 --> 00:33:53,840
Speaker 4: what's the actual impact of this behavioral element of investing,

674
00:33:53,840 --> 00:33:55,440
Speaker 4: which is a really critical part of investing.

675
00:33:55,480 --> 00:33:58,360
Speaker 2: And the gap refers to the behavior gap, which is

676
00:33:58,400 --> 00:34:01,520
Speaker 2: the difference between what the fund generates and what the

677
00:34:01,560 --> 00:34:02,840
Speaker 2: actual investors are getting.

678
00:34:03,000 --> 00:34:04,160
Speaker 3: Yeap, please continue.

679
00:34:04,240 --> 00:34:08,440
Speaker 4: And so what you find is that for like sixty

680
00:34:08,440 --> 00:34:11,320
Speaker 4: to forty balanced funds, which typically are in retirement accounts

681
00:34:11,640 --> 00:34:14,439
Speaker 4: where people maybe aren't looking at them every single day,

682
00:34:14,640 --> 00:34:17,680
Speaker 4: they get statements once a quarter that are delayed.

683
00:34:17,360 --> 00:34:20,240
Speaker 3: Set and forget just it's kind of a set of Yeah.

684
00:34:20,160 --> 00:34:22,680
Speaker 4: That gap is on the order of sixty basis points,

685
00:34:22,840 --> 00:34:26,000
Speaker 4: relatively small, relatively small, but it costs still. It costs

686
00:34:26,000 --> 00:34:28,640
Speaker 4: sixty basis points year for the average investor. This beaver

687
00:34:28,760 --> 00:34:32,120
Speaker 4: for those simple funds. Now for alternative funds, when they

688
00:34:32,160 --> 00:34:34,279
Speaker 4: look at those, that gap is one hundred and seventy

689
00:34:34,320 --> 00:34:35,080
Speaker 4: basis points a year.

690
00:34:35,360 --> 00:34:36,759
Speaker 3: Okay, that's starting it up.

691
00:34:36,800 --> 00:34:38,960
Speaker 4: That really I mean, if you think about that compounding

692
00:34:39,000 --> 00:34:41,960
Speaker 4: over a decade, that was a massive hit to wealth.

693
00:34:42,719 --> 00:34:45,680
Speaker 4: Why is there such a big gap for alternatives and

694
00:34:45,719 --> 00:34:47,600
Speaker 4: not as much of a gap for the sixty forty

695
00:34:47,800 --> 00:34:50,400
Speaker 4: I think it has a lot to do with investor

696
00:34:50,480 --> 00:34:53,400
Speaker 4: understanding of what those products are and therefore the confidence

697
00:34:54,040 --> 00:34:57,759
Speaker 4: people invest in alternatives. They don't necessarily understand them, and

698
00:34:57,840 --> 00:35:00,439
Speaker 4: so you're setting yourself up for FI. You're a little

699
00:35:00,440 --> 00:35:03,319
Speaker 4: bit there, because when it has bad performance you don't

700
00:35:03,400 --> 00:35:05,920
Speaker 4: understand what it does, you're more likely to redeem.

701
00:35:06,160 --> 00:35:07,080
Speaker 3: That makes a lot of sense.

702
00:35:07,120 --> 00:35:10,200
Speaker 4: So to me, investor education, really understanding what they're investing

703
00:35:10,280 --> 00:35:13,080
Speaker 4: is is a critical component to being a successful investor.

704
00:35:13,840 --> 00:35:18,280
Speaker 2: Really really interesting. So you talk a lot about idea meritocracy.

705
00:35:19,440 --> 00:35:22,000
Speaker 2: It's on your site, you've written about it. Explain a

706
00:35:22,000 --> 00:35:24,480
Speaker 2: little bit what is idea meritocracy?

707
00:35:25,239 --> 00:35:27,080
Speaker 4: This is a really important part and it's part of

708
00:35:27,120 --> 00:35:30,360
Speaker 4: our culture at clear Alpha. The idea is to get

709
00:35:30,520 --> 00:35:35,360
Speaker 4: all ideas surfaced so that the organization can make the

710
00:35:35,360 --> 00:35:39,280
Speaker 4: best decisions. How do you know what prevents good ideas

711
00:35:39,280 --> 00:35:42,239
Speaker 4: from surfacing. One is that people may not know that

712
00:35:42,360 --> 00:35:45,480
Speaker 4: you know, a questions even being asked. So many organizations

713
00:35:45,520 --> 00:35:48,960
Speaker 4: are run fairly siloed different groups, and a lot of

714
00:35:49,000 --> 00:35:52,560
Speaker 4: that happens associally large large organizations. It's hard for everybody

715
00:35:52,640 --> 00:35:56,840
Speaker 4: to be constantly communicate with one another, so just not

716
00:35:56,880 --> 00:35:59,040
Speaker 4: even knowing a question exists. So the way we address

717
00:35:59,080 --> 00:36:02,160
Speaker 4: that is that we use Microsoft Teams at the office

718
00:36:02,520 --> 00:36:06,040
Speaker 4: and most people are in various channels and we're seeing

719
00:36:06,120 --> 00:36:09,120
Speaker 4: questions going on all the time. I really discourage people

720
00:36:09,160 --> 00:36:10,960
Speaker 4: from asking me a one on one question. I will

721
00:36:11,000 --> 00:36:14,360
Speaker 4: usually redirect a question someone asked me to here's the

722
00:36:14,360 --> 00:36:17,400
Speaker 4: broad company, here's the question that was asked, here's the answer.

723
00:36:17,719 --> 00:36:21,640
Speaker 4: So then immediately the entire company learns you know what

724
00:36:21,680 --> 00:36:25,720
Speaker 4: this topic was, and very often that says, oh, someone else,

725
00:36:25,880 --> 00:36:28,000
Speaker 4: I have another idea about that that I want to

726
00:36:28,040 --> 00:36:32,000
Speaker 4: now share. So getting accessibility for people to deliver. But

727
00:36:32,040 --> 00:36:35,319
Speaker 4: the most important about idea meritocracy is really from a

728
00:36:35,360 --> 00:36:38,920
Speaker 4: leadership standpoint. People have to feel safe bringing up ideas

729
00:36:39,080 --> 00:36:41,680
Speaker 4: that they're not going to get you know, yelled at.

730
00:36:41,719 --> 00:36:45,960
Speaker 4: You know, there's no bad questions, there's only people not

731
00:36:46,040 --> 00:36:49,480
Speaker 4: asking questions. That's not bad. And the only way that

732
00:36:49,480 --> 00:36:52,480
Speaker 4: that for people to feel safe about that is that

733
00:36:52,520 --> 00:36:55,400
Speaker 4: they need to see me as the leader and my

734
00:36:55,760 --> 00:36:59,719
Speaker 4: other partners as the leaders, to be willing to take

735
00:36:59,760 --> 00:37:03,719
Speaker 4: in feedback, be challenged even publicly and say, you know what,

736
00:37:03,719 --> 00:37:05,719
Speaker 4: that's a really good idea, let's go with that. And

737
00:37:05,760 --> 00:37:07,360
Speaker 4: so just having them feel that safe environment so that

738
00:37:07,440 --> 00:37:10,080
Speaker 4: people can always ask and bring questions up.

739
00:37:10,360 --> 00:37:15,160
Speaker 2: Huh, that's really interesting. Also, you've discussed generating less common

740
00:37:15,200 --> 00:37:19,840
Speaker 2: ideas earlier, we were talking about crowded trades. How do

741
00:37:19,880 --> 00:37:23,520
Speaker 2: you generate less common ideas? How do you find non

742
00:37:23,680 --> 00:37:27,440
Speaker 2: correlated sources of return when you're you know, in a

743
00:37:27,680 --> 00:37:29,400
Speaker 2: hyper competitive marketplace.

744
00:37:29,640 --> 00:37:33,680
Speaker 4: Great question. So I'll use an example here. There's a

745
00:37:33,719 --> 00:37:36,160
Speaker 4: common strategy that people might be familiar with. It's called

746
00:37:36,200 --> 00:37:39,399
Speaker 4: merge arbitrage. And basically, company A is going to buy

747
00:37:39,440 --> 00:37:42,440
Speaker 4: company B, whether it's for cash consideration or stock for

748
00:37:42,480 --> 00:37:47,600
Speaker 4: stock type transaction. And you know, merge arbitragers look at

749
00:37:47,600 --> 00:37:49,440
Speaker 4: that and they might go, you know, long the company's

750
00:37:49,480 --> 00:37:51,600
Speaker 4: being acquired, short, the company's doing the acquire, and then

751
00:37:51,960 --> 00:37:55,160
Speaker 4: make money if that deal ultimately closes. That's a that's

752
00:37:55,200 --> 00:37:58,440
Speaker 4: a very common, well known strategy. That would be the

753
00:37:58,440 --> 00:38:01,360
Speaker 4: common version of implementing the strategy. A less common version

754
00:38:01,480 --> 00:38:04,880
Speaker 4: implemented is you try to find one that you like

755
00:38:05,000 --> 00:38:07,279
Speaker 4: more than others. So you might think they all are

756
00:38:07,320 --> 00:38:09,520
Speaker 4: like the vast majority are going to close, but some

757
00:38:09,719 --> 00:38:11,439
Speaker 4: you might like better than others, and so you could

758
00:38:11,760 --> 00:38:13,760
Speaker 4: go long half of them and short half of them,

759
00:38:14,000 --> 00:38:19,400
Speaker 4: so you're not exposed to this common element of merger

760
00:38:19,480 --> 00:38:25,440
Speaker 4: arbitrage deals closing. You're neutral to those. So if a

761
00:38:25,520 --> 00:38:29,240
Speaker 4: large pod shop, you know, one of these large multi managers,

762
00:38:29,280 --> 00:38:31,040
Speaker 4: if they decided to get out of merger arbitrage and

763
00:38:31,080 --> 00:38:34,000
Speaker 4: they're selling all these positions down, half your portfolio will

764
00:38:34,040 --> 00:38:36,000
Speaker 4: get helped and half your portfolio will get hurt, but

765
00:38:36,000 --> 00:38:39,280
Speaker 4: you're less exposed to that crowding risk, and that common

766
00:38:39,560 --> 00:38:41,480
Speaker 4: what I would say, a risk factor that these other

767
00:38:42,000 --> 00:38:44,520
Speaker 4: common strategies have. So that's a niche version of how

768
00:38:44,520 --> 00:38:46,120
Speaker 4: we might implement that kind of a strategy.

769
00:38:46,400 --> 00:38:47,520
Speaker 3: You mentioned niche.

770
00:38:47,880 --> 00:38:50,640
Speaker 2: I never heard the phrase prior to reading something you

771
00:38:50,640 --> 00:38:54,239
Speaker 2: had written called niche alpha. Tell us a little bit

772
00:38:54,280 --> 00:38:55,680
Speaker 2: what niche alpha is.

773
00:38:56,280 --> 00:39:00,520
Speaker 4: That's a great question. The simple answer is you're unlikely

774
00:39:00,560 --> 00:39:03,080
Speaker 4: to have any or much of it in your hedge

775
00:39:03,080 --> 00:39:05,680
Speaker 4: footy portfolio. That's how I would describe it. And so

776
00:39:05,719 --> 00:39:09,120
Speaker 4: it's looking for people that are either implementing common strategies

777
00:39:09,160 --> 00:39:13,400
Speaker 4: in a very different way that makes them less susceptible

778
00:39:13,520 --> 00:39:17,160
Speaker 4: or more immune to people getting out of that strategy,

779
00:39:17,760 --> 00:39:20,399
Speaker 4: or people have a completely different idea of how to

780
00:39:20,640 --> 00:39:22,799
Speaker 4: make money that I haven't heard of before. And I've

781
00:39:22,800 --> 00:39:26,000
Speaker 4: interviewed hundreds, if not thousands of portfolio managers and worked

782
00:39:26,000 --> 00:39:28,319
Speaker 4: with developed many strateges of my own. So it's trying

783
00:39:28,320 --> 00:39:29,759
Speaker 4: to find things that people aren't doing.

784
00:39:30,160 --> 00:39:34,400
Speaker 2: Huh is there given what we know about the efficient

785
00:39:34,600 --> 00:39:39,680
Speaker 2: market hypothesis? And Gene Fama was Cliff Astness's doctoral advisor,

786
00:39:39,719 --> 00:39:43,880
Speaker 2: is that what or mba Cliff was Gensta Yes? So,

787
00:39:44,360 --> 00:39:47,839
Speaker 2: given how mostly efficient the market is, is are there

788
00:39:47,880 --> 00:39:53,320
Speaker 2: really Nietzsches left that have not been discovered? How many

789
00:39:53,360 --> 00:39:56,960
Speaker 2: more opportunities are out there that we don't know about?

790
00:39:57,440 --> 00:40:00,279
Speaker 4: That taps into something we talked about earlier, which is

791
00:40:00,840 --> 00:40:03,160
Speaker 4: there are thousands of ways to make money in the markets.

792
00:40:03,760 --> 00:40:06,200
Speaker 4: There's only dozens of ways to make money and big

793
00:40:06,239 --> 00:40:08,240
Speaker 4: dollar size at scale at scale.

794
00:40:09,200 --> 00:40:14,520
Speaker 2: So these smaller ideas is that where the mostly kind

795
00:40:14,560 --> 00:40:18,440
Speaker 2: of eventually efficient market hasn't quite reached yet.

796
00:40:18,719 --> 00:40:21,360
Speaker 4: Well, it's what I think about is the amount of

797
00:40:21,360 --> 00:40:23,160
Speaker 4: dollars you can make. This is the race. I think

798
00:40:23,160 --> 00:40:25,120
Speaker 4: about the amount of dollars you can make, divided by

799
00:40:25,160 --> 00:40:27,799
Speaker 4: the complexity or how much brain damage you have to

800
00:40:27,840 --> 00:40:29,960
Speaker 4: inflict upon yourself to actually implement the strategy.

801
00:40:30,080 --> 00:40:30,359
Speaker 3: Uh huh.

802
00:40:30,560 --> 00:40:33,320
Speaker 4: A lot of these small stranges, they're complex and difficult

803
00:40:33,320 --> 00:40:37,040
Speaker 4: to do. That might require, you know, some kind of

804
00:40:37,040 --> 00:40:41,239
Speaker 4: new technique that is difficult, are rare to implement, And

805
00:40:41,280 --> 00:40:44,040
Speaker 4: the actual P and L that you can generate profit

806
00:40:44,080 --> 00:40:46,400
Speaker 4: less you can generate is small villid for that effort.

807
00:40:47,320 --> 00:40:49,960
Speaker 2: Small in terms of percentage returns or small in terms

808
00:40:50,000 --> 00:40:52,920
Speaker 2: of dollars. Hey, there's only one hundred million to arbitrage

809
00:40:52,960 --> 00:40:56,880
Speaker 2: away with this, and once that is mined, that's it.

810
00:40:56,600 --> 00:40:57,360
Speaker 3: It's done.

811
00:40:57,440 --> 00:40:59,400
Speaker 4: It's about dollars of P and L you can extract

812
00:40:59,400 --> 00:41:01,920
Speaker 4: from the markets. Percentage returns can be very high for

813
00:41:01,960 --> 00:41:05,120
Speaker 4: these strategies, but I'll give you a sense. You know,

814
00:41:05,719 --> 00:41:08,400
Speaker 4: most other large shops they're going to look for strategies

815
00:41:08,400 --> 00:41:09,960
Speaker 4: that can generate at least one hundred million dollars at

816
00:41:09,960 --> 00:41:11,920
Speaker 4: P and L to make it worth their while to invest.

817
00:41:12,200 --> 00:41:14,560
Speaker 4: We're looking at strategies that are generating ten, twenty thirty

818
00:41:14,600 --> 00:41:15,680
Speaker 4: forty million dollars per year.

819
00:41:16,160 --> 00:41:16,440
Speaker 3: Huh.

820
00:41:16,520 --> 00:41:19,960
Speaker 2: That's really kind of intriguing. So what sort of demand

821
00:41:20,239 --> 00:41:24,799
Speaker 2: is there for lower capacity strategies? I mean, so you

822
00:41:24,880 --> 00:41:27,080
Speaker 2: guys are less than half a billion dollars, You're not

823
00:41:27,960 --> 00:41:32,480
Speaker 2: an enormous funds. Are there more hedge funds looking to

824
00:41:32,520 --> 00:41:36,600
Speaker 2: swim in these ponds or is this something that hey,

825
00:41:36,640 --> 00:41:38,960
Speaker 2: once you cross a certain size, you just have to

826
00:41:39,040 --> 00:41:43,879
Speaker 2: leave behind and stay with those larger capacity, scalable strategies.

827
00:41:44,239 --> 00:41:46,480
Speaker 4: Yeah. I think this is a general thing for all investors,

828
00:41:46,560 --> 00:41:49,440
Speaker 4: not just other hedge funds. Everybody wants to be in

829
00:41:49,480 --> 00:41:51,040
Speaker 4: the interesting things. They want to be in the lower

830
00:41:51,120 --> 00:41:54,920
Speaker 4: capacity things. They know that they're less crowded, the difficulty

831
00:41:54,920 --> 00:41:56,360
Speaker 4: and really what I think are kind of our business

832
00:41:56,360 --> 00:41:58,640
Speaker 4: model is is you're paying for us to go out

833
00:41:58,640 --> 00:42:00,760
Speaker 4: and search the world and source them because it's expensive.

834
00:42:00,800 --> 00:42:03,960
Speaker 4: It's expensive exercise to do. People might not have the

835
00:42:04,000 --> 00:42:07,279
Speaker 4: expertise or the background to underwrite these types of strategies.

836
00:42:07,760 --> 00:42:09,080
Speaker 4: It takes a lot of work, and at the end

837
00:42:09,120 --> 00:42:12,800
Speaker 4: of the day, alpha is either about being smarter or

838
00:42:12,840 --> 00:42:16,600
Speaker 4: working harder. The being smarter can work in the short term,

839
00:42:16,600 --> 00:42:18,879
Speaker 4: but eventually that does get our way. Eventually someone smart

840
00:42:18,920 --> 00:42:21,680
Speaker 4: enough comes by. The working harder, to me is the

841
00:42:21,719 --> 00:42:22,520
Speaker 4: thing that actually stays.

842
00:42:23,360 --> 00:42:26,400
Speaker 2: Huh, that's really interesting. You would think if the incentive

843
00:42:26,480 --> 00:42:30,080
Speaker 2: was there enough, people would just eventually grind away in

844
00:42:30,120 --> 00:42:30,680
Speaker 2: that space.

845
00:42:30,800 --> 00:42:33,799
Speaker 4: I mean, the incentive is there, it's just not enough

846
00:42:33,800 --> 00:42:35,480
Speaker 4: to be worth the time. And so if you are

847
00:42:35,560 --> 00:42:39,360
Speaker 4: a very large investor of organization, you do have to

848
00:42:39,400 --> 00:42:43,200
Speaker 4: prioritize you still have limited resources in time to look

849
00:42:43,239 --> 00:42:46,280
Speaker 4: for things, so you're going to have you know, thresholds.

850
00:42:46,280 --> 00:42:47,799
Speaker 4: I'm not going to invest at least, you know, at

851
00:42:47,800 --> 00:42:50,480
Speaker 4: this amount of dollars, and that's where we step in

852
00:42:50,600 --> 00:42:51,560
Speaker 4: is kind of fill that gap.

853
00:42:51,960 --> 00:42:54,160
Speaker 2: So you're very much a student of what's going on

854
00:42:54,239 --> 00:42:57,160
Speaker 2: in the hedge fund world. What are you seeing in

855
00:42:57,239 --> 00:43:02,040
Speaker 2: terms of strategies driving cost down and the question of

856
00:43:02,080 --> 00:43:05,480
Speaker 2: where fees are. They've certainly pulled back from the days

857
00:43:05,520 --> 00:43:09,080
Speaker 2: of two and twenty. What's happening in terms of efficiency

858
00:43:09,120 --> 00:43:09,640
Speaker 2: and cost.

859
00:43:10,040 --> 00:43:11,799
Speaker 4: There's a bunch of things to talk about there. So

860
00:43:12,040 --> 00:43:14,719
Speaker 4: The first thing I would say is the higher capacity

861
00:43:14,760 --> 00:43:17,400
Speaker 4: strategies that have become well known. I think that those

862
00:43:18,200 --> 00:43:19,919
Speaker 4: costs are going down because there's a lot of people

863
00:43:19,920 --> 00:43:21,600
Speaker 4: who can implement those strategies and so that you think

864
00:43:21,600 --> 00:43:24,719
Speaker 4: just simple supply and demand, lots of portfolio managers you

865
00:43:24,760 --> 00:43:26,480
Speaker 4: can do them, and so then it's just a competition

866
00:43:26,480 --> 00:43:28,200
Speaker 4: of who's going to be able to do it most efficiently.

867
00:43:29,160 --> 00:43:32,920
Speaker 4: Then there's unique alpha. I think that's harder, and actually

868
00:43:32,960 --> 00:43:34,799
Speaker 4: the cost of that has gone up over time. It's

869
00:43:34,840 --> 00:43:38,040
Speaker 4: not gone down. The cost it takes to compete in

870
00:43:38,080 --> 00:43:42,160
Speaker 4: the space has increased over time. So there's a bifurcation

871
00:43:42,200 --> 00:43:45,759
Speaker 4: that's been going on. We think that there's still a

872
00:43:45,800 --> 00:43:48,239
Speaker 4: lot of efficiencies you can carve out of the system

873
00:43:48,440 --> 00:43:52,200
Speaker 4: that exists now that we're attacking lot through technology, a

874
00:43:52,239 --> 00:43:54,319
Speaker 4: lot of three ways of working that can just make

875
00:43:54,320 --> 00:43:57,200
Speaker 4: the organization more efficient and deliver more net returns to investors.

876
00:43:57,320 --> 00:44:01,000
Speaker 2: So we've seen some motion towards fees for alpha and

877
00:44:01,040 --> 00:44:03,960
Speaker 2: not betos. Some people call it pivot fees. There's like

878
00:44:04,000 --> 00:44:06,759
Speaker 2: a lot of different names for this. I haven't heard

879
00:44:06,800 --> 00:44:10,440
Speaker 2: much about that recently. What are your thoughts on where

880
00:44:10,640 --> 00:44:12,360
Speaker 2: hedge fund fees are going in the future.

881
00:44:13,120 --> 00:44:16,920
Speaker 4: I'll answer that with a different story that we'll draw

882
00:44:16,960 --> 00:44:20,359
Speaker 4: on analogy here with the rise of indexing, which has

883
00:44:20,400 --> 00:44:23,160
Speaker 4: been happening for decades now, and thank god for indexing.

884
00:44:23,200 --> 00:44:27,640
Speaker 4: It's a fantastic invention that has helped a lot of investors.

885
00:44:28,280 --> 00:44:31,960
Speaker 4: The original thought was, well, as the market goes more

886
00:44:31,960 --> 00:44:33,640
Speaker 4: and more indexing, and I don't know what the number is,

887
00:44:33,680 --> 00:44:37,480
Speaker 4: it's probably seventy percent is indexed of the invested dollars,

888
00:44:38,800 --> 00:44:41,040
Speaker 4: then it makes the markets, you know, it's easier to

889
00:44:41,160 --> 00:44:43,200
Speaker 4: make money because there's less people trying to compete for that.

890
00:44:44,200 --> 00:44:46,799
Speaker 4: But that's not what actually happens. What actually happens is

891
00:44:47,360 --> 00:44:51,359
Speaker 4: it's become more and more difficult to make money because

892
00:44:51,680 --> 00:44:54,880
Speaker 4: the talent pool is of higher quality now than it

893
00:44:54,960 --> 00:44:58,279
Speaker 4: used to be. That's searching for that alpha. And just

894
00:44:58,440 --> 00:45:04,759
Speaker 4: like sport, it's when there's a zero sum game right right,

895
00:45:05,560 --> 00:45:08,200
Speaker 4: and it's just it's very small differences between you know,

896
00:45:08,760 --> 00:45:11,719
Speaker 4: the number one person and the number five person. What

897
00:45:11,800 --> 00:45:15,960
Speaker 4: you see is the rewards and the compensation tends to

898
00:45:16,000 --> 00:45:19,560
Speaker 4: be a power law, meaning that the very few get

899
00:45:19,600 --> 00:45:22,400
Speaker 4: get paid a lot. And I see for pure alpha,

900
00:45:22,400 --> 00:45:26,200
Speaker 4: where there's real competition that the investment talent will actually

901
00:45:26,200 --> 00:45:28,160
Speaker 4: get paid more and more over time, it will get

902
00:45:28,200 --> 00:45:31,000
Speaker 4: more and more difficult to be that person. Whereas for

903
00:45:31,120 --> 00:45:34,680
Speaker 4: the common stuff, the well known things that have higher capacity,

904
00:45:34,960 --> 00:45:36,759
Speaker 4: I think you're gonna see fees keep going down.

905
00:45:36,800 --> 00:45:39,799
Speaker 2: On that side, Michael Mobison calls that the paradox of

906
00:45:39,880 --> 00:45:43,800
Speaker 2: skill that the more skillful the players are, whether it's sports,

907
00:45:44,239 --> 00:45:49,120
Speaker 2: investing business, the more of a role luck plays, which

908
00:45:49,160 --> 00:45:52,680
Speaker 2: is really really kind of kind of fascinating. You've also

909
00:45:52,719 --> 00:45:57,360
Speaker 2: written about portable alpha. Discuss discuss portable alpha. What is

910
00:45:57,400 --> 00:45:59,040
Speaker 2: that and how can we get some.

911
00:46:00,000 --> 00:46:02,360
Speaker 4: Think portal alfa is a great way for investors to

912
00:46:02,360 --> 00:46:07,200
Speaker 4: get exposure to alternative return streams. What portal alfa is

913
00:46:07,200 --> 00:46:09,520
Speaker 4: is mixing a beta like s and P five hundred

914
00:46:09,520 --> 00:46:12,400
Speaker 4: exposure with an alpha stream and really just pop in

915
00:46:12,400 --> 00:46:14,760
Speaker 4: that offa stream on top of the SMP five hundred returns.

916
00:46:14,800 --> 00:46:18,880
Speaker 4: So it lets investors get exposure to SMP, which most

917
00:46:18,880 --> 00:46:22,919
Speaker 4: investors already have, but now exposure to a different type

918
00:46:22,920 --> 00:46:26,120
Speaker 4: of return stream. Usually people historically at least have tried

919
00:46:26,120 --> 00:46:28,759
Speaker 4: to beat the SMP by picking a manager who's trying

920
00:46:28,760 --> 00:46:32,400
Speaker 4: to pick stocks, overweighting stocks that they like versus the

921
00:46:32,400 --> 00:46:35,600
Speaker 4: index and underwaitting stocks that they don't like. But that

922
00:46:35,719 --> 00:46:38,440
Speaker 4: comes with a lot of constraints. One is the manager

923
00:46:38,440 --> 00:46:41,000
Speaker 4: can only overweight underweight stocks in the index. They can't

924
00:46:41,000 --> 00:46:45,560
Speaker 4: trade other asset classes, they can't utilize any kind of

925
00:46:45,600 --> 00:46:49,760
Speaker 4: sophisticated investment techniques to try to beat that benchmark. Portal

926
00:46:49,760 --> 00:46:52,759
Speaker 4: alpha get rid of all of those constraints, and so

927
00:46:52,800 --> 00:46:56,200
Speaker 4: what you typically see is portal alpha programs are much

928
00:46:56,200 --> 00:47:00,600
Speaker 4: better and consistently beating traditional active programs.

929
00:47:01,000 --> 00:47:04,759
Speaker 2: I like the phrase Corey Hofstein uses for that return stacking.

930
00:47:05,239 --> 00:47:08,279
Speaker 2: Is that same concept for portable alpha? That's right, Yeah,

931
00:47:08,360 --> 00:47:12,920
Speaker 2: really really interesting. Before I get to my favorite questions

932
00:47:12,920 --> 00:47:15,960
Speaker 2: that I ask, well, my guests, I just have to

933
00:47:16,000 --> 00:47:19,239
Speaker 2: throw you a a little bit of a curveball. So

934
00:47:19,360 --> 00:47:23,920
Speaker 2: you're a member of the Yale New Haven Children's Hospital Council,

935
00:47:24,280 --> 00:47:25,920
Speaker 2: tell us a little bit about what you do with that.

936
00:47:26,600 --> 00:47:29,000
Speaker 4: Sure. So just how we got involved, My wife and

937
00:47:29,000 --> 00:47:31,640
Speaker 4: I we with the five kids, three of which had

938
00:47:31,719 --> 00:47:35,279
Speaker 4: severe peenaut allergies, and we were very concerned about that.

939
00:47:35,520 --> 00:47:41,080
Speaker 4: You know, that's become a rising epidemic within a society

940
00:47:41,080 --> 00:47:43,680
Speaker 4: over time, and we wanted to see if we could

941
00:47:43,680 --> 00:47:47,360
Speaker 4: solve that invest in basically research, try to solve this problem.

942
00:47:47,680 --> 00:47:51,000
Speaker 4: So we work with both Yale and our local hospital too.

943
00:47:51,040 --> 00:47:54,360
Speaker 4: Can we fund a research effort and a clinical effort

944
00:47:54,600 --> 00:47:57,120
Speaker 4: to basically collect data because a lot of the research

945
00:47:57,160 --> 00:47:59,279
Speaker 4: really needs data, So we work with them. That's how

946
00:47:59,280 --> 00:48:03,799
Speaker 4: we got originally with yellows an organization, and then they

947
00:48:03,800 --> 00:48:07,880
Speaker 4: have this council that's focused on children's health issues and

948
00:48:08,680 --> 00:48:10,480
Speaker 4: what it is. It's a collection of individuals who are

949
00:48:10,480 --> 00:48:14,319
Speaker 4: interested in this topic. We meet typically quarterly. They'll have,

950
00:48:14,440 --> 00:48:16,359
Speaker 4: you know, some of their top researchers from Yale come

951
00:48:16,360 --> 00:48:19,279
Speaker 4: in and talk about whatever research they're working on and

952
00:48:19,480 --> 00:48:24,200
Speaker 4: their clinical experiences with you know, children as patients, and

953
00:48:24,400 --> 00:48:26,640
Speaker 4: that usually generates ideas, Okay, how can we make this

954
00:48:27,200 --> 00:48:29,800
Speaker 4: more effective? How can we get more funds directed towards

955
00:48:29,840 --> 00:48:30,480
Speaker 4: this activity?

956
00:48:30,680 --> 00:48:33,200
Speaker 2: All right, we only have you for a couple of minutes.

957
00:48:33,280 --> 00:48:36,839
Speaker 2: Let's jump to my favorite questions that we ask all

958
00:48:36,880 --> 00:48:40,320
Speaker 2: of our guests, starting with what are you streaming these days?

959
00:48:40,320 --> 00:48:44,520
Speaker 2: What's keeping you entertained? Either Netflix, podcast, Amazon, whatever.

960
00:48:45,320 --> 00:48:47,680
Speaker 4: My wife and I, after going through the litany of

961
00:48:47,680 --> 00:48:50,080
Speaker 4: all the kids and their issues each day, it's usually

962
00:48:50,120 --> 00:48:52,120
Speaker 4: very late and so we don't get to watch as

963
00:48:52,160 --> 00:48:53,640
Speaker 4: much TV as probably would like. There's a lot of

964
00:48:53,640 --> 00:48:58,160
Speaker 4: great content out there. Lately, we're watching Lionis on Paramount,

965
00:48:58,200 --> 00:48:58,760
Speaker 4: which is.

966
00:48:58,640 --> 00:49:00,920
Speaker 2: I just finished season one of few weeks ago and

967
00:49:01,360 --> 00:49:02,960
Speaker 2: taking a break before season two.

968
00:49:03,000 --> 00:49:03,880
Speaker 3: But it's fantastic.

969
00:49:03,920 --> 00:49:05,960
Speaker 4: It's fantastic. Yeah, we've really enjoyed it so far.

970
00:49:06,960 --> 00:49:09,560
Speaker 3: But I would say, are you up to season two yet?

971
00:49:09,760 --> 00:49:12,759
Speaker 4: No, we're three or four episodes in. Oh, this season one?

972
00:49:13,040 --> 00:49:14,480
Speaker 3: Brace yourself, you have quite right?

973
00:49:14,760 --> 00:49:19,080
Speaker 4: Okay, great, But in terms of like favorite shows, one

974
00:49:19,080 --> 00:49:22,000
Speaker 4: of my favorites was the remake of Battlestar Galactica, which

975
00:49:22,040 --> 00:49:24,240
Speaker 4: was a show when I was growing up as a kid.

976
00:49:24,440 --> 00:49:27,319
Speaker 2: With a terrible special effects in the old one, yes,

977
00:49:27,400 --> 00:49:28,600
Speaker 2: and the new one is great.

978
00:49:28,760 --> 00:49:31,520
Speaker 4: Right, that's right. And there's there's a scene that's actually

979
00:49:31,520 --> 00:49:35,520
Speaker 4: relevant to our conversation a little bit today. The leader

980
00:49:35,520 --> 00:49:39,160
Speaker 4: of the sidelines, which is like the robots, is talking

981
00:49:39,200 --> 00:49:42,399
Speaker 4: with Human is one of the fighter pilots, and they're

982
00:49:42,440 --> 00:49:46,040
Speaker 4: watching a video of one of the battles and the

983
00:49:46,120 --> 00:49:48,960
Speaker 4: humans win this battle. But then the sieline says, this

984
00:49:49,000 --> 00:49:51,040
Speaker 4: is how we're going to beat you, and Human's like,

985
00:49:51,400 --> 00:49:54,239
Speaker 4: what do you mean? Because they just watched, like one

986
00:49:54,320 --> 00:49:57,960
Speaker 4: of the humans kill one of the robot fighter pilots,

987
00:49:58,560 --> 00:50:02,160
Speaker 4: and she says, well, every time that we make a

988
00:50:02,200 --> 00:50:07,800
Speaker 4: mistake and we lose a battle, every single other Cylon

989
00:50:08,640 --> 00:50:12,799
Speaker 4: learns from that, and so inevitably we will learn every

990
00:50:12,800 --> 00:50:17,239
Speaker 4: way that we can avoid dying and we will take

991
00:50:17,239 --> 00:50:19,319
Speaker 4: you over. And that has a lot to do with

992
00:50:20,480 --> 00:50:23,840
Speaker 4: how we approach the business on the investing side, always

993
00:50:23,880 --> 00:50:28,760
Speaker 4: learn from mistakes, get the communication out there, and constantly improve.

994
00:50:28,800 --> 00:50:31,359
Speaker 4: If you improve by a few percent a year, that

995
00:50:31,400 --> 00:50:32,480
Speaker 4: really compounds over time.

996
00:50:32,719 --> 00:50:34,840
Speaker 2: Well, what does it matter if the AI silon is

997
00:50:34,840 --> 00:50:37,080
Speaker 2: eventually going to kill all of us, It won't make

998
00:50:37,120 --> 00:50:42,040
Speaker 2: any difference. Alpha is only here until the cylons beat

999
00:50:42,120 --> 00:50:43,240
Speaker 2: us in a space battle.

1000
00:50:44,360 --> 00:50:47,200
Speaker 3: We view it that's way off in the distance.

1001
00:50:47,840 --> 00:50:53,239
Speaker 4: We like intelligence augmentation versus artificial intelligence IA instead of

1002
00:50:53,280 --> 00:50:56,120
Speaker 4: AI using these tools to be more effective.

1003
00:50:56,160 --> 00:50:59,600
Speaker 2: That makes a lot of sense. Let's talk about your

1004
00:50:59,640 --> 00:51:02,000
Speaker 2: mentor who helped to shape your career.

1005
00:51:02,920 --> 00:51:05,960
Speaker 4: Well, I would say of all the ones I could

1006
00:51:05,960 --> 00:51:09,840
Speaker 4: think of, Cliff would be the top mentor. And Cliff

1007
00:51:09,920 --> 00:51:11,359
Speaker 4: wasn't the kind of guy who would you know put

1008
00:51:11,440 --> 00:51:13,759
Speaker 4: your brand? Is his arm around you say hey, you

1009
00:51:13,760 --> 00:51:15,239
Speaker 4: know this, you do X, Y and Z, and you

1010
00:51:15,239 --> 00:51:18,040
Speaker 4: should do this differently. He did have a good several

1011
00:51:18,040 --> 00:51:20,799
Speaker 4: conversations with me like that. Most of his mentorship was

1012
00:51:20,800 --> 00:51:25,960
Speaker 4: through his actions. Clip's extremely principled, very ethical, and it's

1013
00:51:26,680 --> 00:51:28,520
Speaker 4: it's a very fortunate thing to be able to be

1014
00:51:28,560 --> 00:51:31,520
Speaker 4: in business with someone like that, where you can be

1015
00:51:31,600 --> 00:51:34,160
Speaker 4: successful at business but do it in a very ethical,

1016
00:51:34,200 --> 00:51:36,799
Speaker 4: principled way that's always doing right by the client, and

1017
00:51:36,840 --> 00:51:39,080
Speaker 4: that's something some of the biggest things I've taken away

1018
00:51:39,160 --> 00:51:39,920
Speaker 4: from working with him.

1019
00:51:40,320 --> 00:51:42,360
Speaker 2: Let's talk about books. What are some of your favorites

1020
00:51:42,400 --> 00:51:43,600
Speaker 2: and what are you reading right now?

1021
00:51:44,960 --> 00:51:49,680
Speaker 4: I like history, specifically financial history. The one I'm reading

1022
00:51:49,760 --> 00:51:52,560
Speaker 4: right now is called The World for Sale. It's actually

1023
00:51:52,560 --> 00:51:55,840
Speaker 4: written by a couple of journalists that cover the commodity industry,

1024
00:51:56,000 --> 00:51:57,840
Speaker 4: and it's really about the physical commodity traders and the

1025
00:51:57,840 --> 00:51:59,960
Speaker 4: whole history of that, which is which is kind of interesting.

1026
00:52:00,480 --> 00:52:04,600
Speaker 4: I love biographies. One of particularly liked was the Michael

1027
00:52:04,640 --> 00:52:07,360
Speaker 4: Dell one Played Nice but When where it's kind of

1028
00:52:07,440 --> 00:52:11,319
Speaker 4: chronologically it's this whole story. I really connected with the

1029
00:52:11,360 --> 00:52:13,640
Speaker 4: building computers in his dorm and selling them. Obviously, he

1030
00:52:13,640 --> 00:52:16,480
Speaker 4: was much more successful at that than I was really interesting.

1031
00:52:16,560 --> 00:52:19,920
Speaker 2: Any chance you read McCullough's Wright Brothers, I have not

1032
00:52:20,600 --> 00:52:25,080
Speaker 2: really fascinating. I like, it's unusual to read something that

1033
00:52:25,160 --> 00:52:27,759
Speaker 2: you think, oh, I know that history, and then it's like, no,

1034
00:52:27,840 --> 00:52:29,680
Speaker 2: you have no idea what's going on in the history.

1035
00:52:29,920 --> 00:52:33,040
Speaker 2: And he's just a great writer, really really really interesting.

1036
00:52:33,680 --> 00:52:36,880
Speaker 2: Our final two questions, what sort of advice would you

1037
00:52:36,920 --> 00:52:40,880
Speaker 2: give to a recent college grad interested in a career

1038
00:52:41,000 --> 00:52:43,960
Speaker 2: in either quantitative or investment finance.

1039
00:52:45,560 --> 00:52:47,160
Speaker 4: I don't know if the advice would be specific to

1040
00:52:47,239 --> 00:52:50,880
Speaker 4: those things. But talk less and listen more, that is

1041
00:52:50,880 --> 00:52:55,520
Speaker 4: what I would say. There's a curve. I forget the

1042
00:52:55,600 --> 00:52:58,959
Speaker 4: name of the curve, but it's you know, you start

1043
00:52:59,000 --> 00:53:02,160
Speaker 4: thinking you know a lot, especially Unny Kruger. Yeah, Dunning Kriger,

1044
00:53:02,239 --> 00:53:05,440
Speaker 4: that's what it is. Yeah, that is such a true effect.

1045
00:53:06,040 --> 00:53:08,920
Speaker 4: I thought I knew everything, and if I just listened

1046
00:53:08,960 --> 00:53:12,720
Speaker 4: to those around me who knew a lot more people

1047
00:53:12,719 --> 00:53:15,440
Speaker 4: are trying to help you more than you realize as

1048
00:53:15,440 --> 00:53:17,120
Speaker 4: a young person, and I should have just listened to

1049
00:53:17,120 --> 00:53:21,080
Speaker 4: more advice. I would have been more successful, much more

1050
00:53:21,120 --> 00:53:22,080
Speaker 4: earlier if I had.

1051
00:53:22,600 --> 00:53:25,920
Speaker 2: So here's the funny thing about the Dunning Kruger curve,

1052
00:53:26,160 --> 00:53:29,520
Speaker 2: and this comes straight from David Dunning. They did not

1053
00:53:29,640 --> 00:53:33,080
Speaker 2: create the Dunning Kruger curve. It kind of came from

1054
00:53:33,560 --> 00:53:37,160
Speaker 2: just pop psychology and social media. And then when they

1055
00:53:37,200 --> 00:53:40,400
Speaker 2: went back and tested it, I think the paper was

1056
00:53:40,440 --> 00:53:43,279
Speaker 2: like ninety nine or two thousand and four, something like that.

1057
00:53:43,560 --> 00:53:45,799
Speaker 2: When they went back and tested it, it turned out

1058
00:53:46,080 --> 00:53:49,359
Speaker 2: that the Dunning Kruger curve turned out to be a realistic,

1059
00:53:49,920 --> 00:53:56,440
Speaker 2: measurable effect. And it's mount stupid. The valley of Despair

1060
00:53:56,840 --> 00:54:00,040
Speaker 2: and the slope of enlightenment are just sort of the

1061
00:54:00,040 --> 00:54:03,120
Speaker 2: the pop terms of it, but it's really really funny.

1062
00:54:03,920 --> 00:54:06,480
Speaker 2: And our final question, what do you know about the

1063
00:54:06,520 --> 00:54:09,799
Speaker 2: world of investing today? You wish you knew back in

1064
00:54:09,880 --> 00:54:13,560
Speaker 2: the early nineties that would have been helpful to you

1065
00:54:13,640 --> 00:54:14,640
Speaker 2: over those decades.

1066
00:54:15,920 --> 00:54:19,080
Speaker 4: There's a lot of smart people out there, as smart

1067
00:54:19,120 --> 00:54:21,600
Speaker 4: as you might be. There's a lot to learn from

1068
00:54:21,600 --> 00:54:26,719
Speaker 4: everybody else. Everybody has some insight, some perspective that you

1069
00:54:26,760 --> 00:54:32,800
Speaker 4: don't have. Don't presume how you know what people are thinking.

1070
00:54:33,640 --> 00:54:36,000
Speaker 4: So ask questions and listen.

1071
00:54:36,440 --> 00:54:40,320
Speaker 2: Sounds like good advice for everybody. We have been speaking

1072
00:54:40,320 --> 00:54:43,520
Speaker 2: with Brian Hurst. He's the founder and CIO of Clear

1073
00:54:43,560 --> 00:54:47,000
Speaker 2: Alpha If you enjoy this conversation, well, be sure and

1074
00:54:47,080 --> 00:54:50,120
Speaker 2: check out any of the five hundred and thirty we've

1075
00:54:50,160 --> 00:54:52,799
Speaker 2: done over the past ten years. You can find those

1076
00:54:52,800 --> 00:54:58,080
Speaker 2: at iTunes, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts,

1077
00:54:58,400 --> 00:55:01,720
Speaker 2: be sure and check out my latest podcast, At the Money,

1078
00:55:02,280 --> 00:55:06,720
Speaker 2: short ten minute conversations with experts about topics that affect

1079
00:55:06,800 --> 00:55:10,719
Speaker 2: your money, spending it, earning it, and most importantly, investing it.

1080
00:55:11,160 --> 00:55:14,480
Speaker 2: At the Money. Wherever you find your favorite podcasts, I

1081
00:55:14,480 --> 00:55:16,319
Speaker 2: would be remiss if it and not thank the crack

1082
00:55:16,440 --> 00:55:19,240
Speaker 2: team that helps us put these conversations together each week.

1083
00:55:19,480 --> 00:55:24,160
Speaker 2: Sarah Livesey is my audio engineer. Sage Bauman is the

1084
00:55:24,160 --> 00:55:28,560
Speaker 2: head of podcasts. Sean Russo is my researcher. Anna Luca

1085
00:55:28,600 --> 00:55:29,440
Speaker 2: is my producer.

1086
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Speaker 3: I'm Barry Retolts.

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Speaker 2: You've been listening to Masters in Business on Bloomberg Radio.